MotiMul

MotiMul identifies significant sequence motifs by combining Tarone's multiple-testing correction with a PrefixSpan-based enumeration to control type-1 error while preserving statistical power for motif discovery.

Key Features:

• Statistically Sound Multiple Testing Correction: Incorporates multiple testing correction methods to control type-1 error while avoiding overly stringent adjustments that reduce statistical power.
• Tarone’s Correction Methodology: Employs Tarone's correction to disregard hypotheses unlikely to reach significance and thereby enhance statistical power.
• Integration with PrefixSpan Algorithm: Integrates a variant of the PrefixSpan algorithm to efficiently enumerate sequence motifs.
• Efficient Enumeration of Significant Motifs: Combines Tarone's correction with the PrefixSpan variant to efficiently identify significant sequence motifs.

Scientific Applications:

• Motif discovery: Identifies biologically meaningful sequence motifs to provide insights into gene regulation and protein-DNA interactions.
• Statistical analysis of datasets: Analyzes simulated and empirical datasets while controlling type-1 error and maintaining high statistical power.

Methodology:

Combines Tarone's correction with an adapted PrefixSpan algorithm to enumerate and test sequence motifs.